Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2513-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-2513-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2019
Research article |
| 13 Nov 2019
| 13 Nov 2019
Bayesian network model for flood forecasting based on atmospheric ensemble forecasts
Leila Goodarzi
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Mohammad E. Banihabib
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Abbas Roozbahani
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Jörg Dietrich
CORRESPONDING AUTHOR
Institute of Hydrology and Water Resources Management, Leibniz
University Hannover, Hanover, Germany
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Jörg Dietrich
Proc. IAHS, 373, 19–24, https://doi.org/10.5194/piahs-373-19-2016, https://doi.org/10.5194/piahs-373-19-2016, 2016
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This research article presents finding from an interdisciplinary joint research project in integrated river basin management. For the implementation of the EU Water Framework Directive, measures to improve the hydromorphological situation and to reduce nutrient emissions have been planned at water body scale. We used a strategy approach to formulate alternative solutions for the whole basin according to policy directions. The results should inform and support stakeholders and decision makers.
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Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event
Water depth estimate and flood extent enhancement for satellite-based inundation maps
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A downward-counterfactual analysis of flash floods in Germany
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Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian M. Grams
Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, https://doi.org/10.5194/nhess-24-2995-2024, 2024
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Extreme rainfall is the leading weather-related source of damages in Europe, but it is still difficult to predict on long timescales. A recent example of this was the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allows us to better understand and predict such events.
Alison L. Kay, Nick Dunstone, Gillian Kay, Victoria A. Bell, and Jamie Hannaford
Nat. Hazards Earth Syst. Sci., 24, 2953–2970, https://doi.org/10.5194/nhess-24-2953-2024, https://doi.org/10.5194/nhess-24-2953-2024, 2024
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Hydrological hazards affect people and ecosystems, but extremes are not fully understood due to limited observations. A large climate ensemble and simple hydrological model are used to assess unprecedented but plausible floods and droughts. The chain gives extreme flows outside the observed range: summer 2022 ~ 28 % lower and autumn 2023 ~ 42 % higher. Spatial dependence and temporal persistence are analysed. Planning for such events could help water supply resilience and flood risk management.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
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Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
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Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, https://doi.org/10.5194/nhess-24-2817-2024, 2024
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The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 24, 2647–2665, https://doi.org/10.5194/nhess-24-2647-2024, https://doi.org/10.5194/nhess-24-2647-2024, 2024
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This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
Nat. Hazards Earth Syst. Sci., 24, 2577–2595, https://doi.org/10.5194/nhess-24-2577-2024, https://doi.org/10.5194/nhess-24-2577-2024, 2024
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Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.
Helge Bormann, Jenny Kebschull, Lidia Gaslikova, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 2559–2576, https://doi.org/10.5194/nhess-24-2559-2024, https://doi.org/10.5194/nhess-24-2559-2024, 2024
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Inland flooding is threatening coastal lowlands. If rainfall and storm surges coincide, the risk of inland flooding increases. We examine how such compound events are influenced by climate change. Data analysis and model-based scenario analysis show that climate change induces an increasing frequency and intensity of compounding precipitation and storm tide events along the North Sea coast. Overload of inland drainage systems will also increase if no timely adaptation measures are taken.
Yanxia Shen, Zhenduo Zhu, Qi Zhou, and Chunbo Jiang
Nat. Hazards Earth Syst. Sci., 24, 2315–2330, https://doi.org/10.5194/nhess-24-2315-2024, https://doi.org/10.5194/nhess-24-2315-2024, 2024
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We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify flood-prone areas where high-resolution inundation conditions are needed, and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. The improved model was reliable.
Taylor Glen Johnson, Jorge Leandro, and Divine Kwaku Ahadzie
Nat. Hazards Earth Syst. Sci., 24, 2285–2302, https://doi.org/10.5194/nhess-24-2285-2024, https://doi.org/10.5194/nhess-24-2285-2024, 2024
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Reliance on infrastructure creates vulnerabilities to disruptions caused by natural hazards. To assess the impacts of natural hazards on the performance of infrastructure, we present a framework for quantifying resilience and develop a model of recovery based upon an application of project scheduling under resource constraints. The resilience framework and recovery model were applied in a case study to assess the resilience of building infrastructure to flooding hazards in Accra, Ghana.
Arnau Amengual, Romu Romero, María Carmen Llasat, Alejandro Hermoso, and Montserrat Llasat-Botija
Nat. Hazards Earth Syst. Sci., 24, 2215–2242, https://doi.org/10.5194/nhess-24-2215-2024, https://doi.org/10.5194/nhess-24-2215-2024, 2024
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On 22 October 2019, the Francolí River basin experienced a heavy precipitation event, resulting in a catastrophic flash flood. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, https://doi.org/10.5194/nhess-24-2147-2024, 2024
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To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management.
Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, https://doi.org/10.5194/nhess-24-2071-2024, 2024
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A methodology of regional flood hazard mapping is proposed, based on data in Austria, which combines automatic methods with manual interventions to maximise efficiency and to obtain estimation accuracy similar to that of local studies. Flood discharge records from 781 stations are used to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. The hazard maps are used for civil protection, risk awareness and insurance purposes.
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, https://doi.org/10.5194/nhess-24-1975-2024, 2024
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The simultaneous occurrence of meteorological (precipitation), agricultural (soil moisture), and hydrological (streamflow) drought can lead to augmented impacts. By analysing drought indices derived from the newest climate scenarios for Switzerland (CH2018, Hydro-CH2018), we show that with climate change the concurrence of all drought types will increase in all studied regions of Switzerland. Our results stress the benefits of and need for both mitigation and adaptation measures at early stages.
Melody Gwyneth Whitehead and Mark Stephen Bebbington
Nat. Hazards Earth Syst. Sci., 24, 1929–1935, https://doi.org/10.5194/nhess-24-1929-2024, https://doi.org/10.5194/nhess-24-1929-2024, 2024
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Precipitation-driven hazards including floods, landslides, and lahars can be catastrophic and difficult to forecast due to high uncertainty around future weather patterns. This work presents a stochastic weather model that produces statistically similar (realistic) rainfall over long time periods at minimal computational cost. These data provide much-needed inputs for hazard simulations to support long-term, time and spatially varying risk assessments.
Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito
Nat. Hazards Earth Syst. Sci., 24, 1757–1777, https://doi.org/10.5194/nhess-24-1757-2024, https://doi.org/10.5194/nhess-24-1757-2024, 2024
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We delved into the socio-economic impacts of the 2018–2022 drought in Germany. We derived a dataset covering the impacts of droughts in Germany between 2000 and 2022 on sectors such as agriculture and forestry based on newspaper articles. Notably, our study illustrated that the longer drought had a wider reach and more varied effects. We show that dealing with longer droughts requires different plans compared to shorter ones, and it is crucial to be ready for the challenges they bring.
Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
Nat. Hazards Earth Syst. Sci., 24, 1681–1696, https://doi.org/10.5194/nhess-24-1681-2024, https://doi.org/10.5194/nhess-24-1681-2024, 2024
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INSYDE 2.0 is a tool for modelling flood damage to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.
Melissa Wood, Ivan D. Haigh, Quan Quan Le, Hung Nghia Nguyen, Hoang Tran Ba, Stephen E. Darby, Robert Marsh, Nikolaos Skliris, and Joël J.-M. Hirschi
EGUsphere, https://doi.org/10.5194/egusphere-2024-949, https://doi.org/10.5194/egusphere-2024-949, 2024
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We look at how compound flooding from the combination of river flooding and storm tide (storm surge plus astronomical tide) may be changing over time due to climate change, with a case study of the Mekong River delta. We found that future compound flooding has potential to flood the region more extensively and be longer lasting than compound floods today. This is useful to know because it means that managers of deltas such as the Mekong can assess options for improving existing flood defences.
Théo St. Pierre Ostrander, Thomé Kraus, Bruno Mazzorana, Johannes Holzner, Andrea Andreoli, Francesco Comiti, and Bernhard Gems
Nat. Hazards Earth Syst. Sci., 24, 1607–1634, https://doi.org/10.5194/nhess-24-1607-2024, https://doi.org/10.5194/nhess-24-1607-2024, 2024
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Mountain river confluences are hazardous during localized flooding events. A physical model was used to determine the dominant controls over mountain confluences. Contrary to lowland confluences, in mountain regions, the channel discharges and (to a lesser degree) the tributary sediment concentration control morphological patterns. Applying conclusions drawn from lowland confluences could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.
Sheik Umar Jam-Jalloh, Jia Liu, Yicheng Wang, and Yuchen Liu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-20, https://doi.org/10.5194/nhess-2024-20, 2024
Revised manuscript accepted for NHESS
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Explore our paper on improving flood prediction using advanced weather models. We coupled the WRF model with WRF-Hydro and HEC-HMS to enhance accuracy. Discover how our findings contribute to adaptive atmospheric-hydrologic systems for effective flood forecasting.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
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High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Wilson C. H. Chan, Nigel W. Arnell, Geoff Darch, Katie Facer-Childs, Theodore G. Shepherd, and Maliko Tanguy
Nat. Hazards Earth Syst. Sci., 24, 1065–1078, https://doi.org/10.5194/nhess-24-1065-2024, https://doi.org/10.5194/nhess-24-1065-2024, 2024
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The most recent drought in the UK was declared in summer 2022. We pooled a large sample of plausible winters from seasonal hindcasts and grouped them into four clusters based on their atmospheric circulation configurations. Drought storylines representative of what the drought could have looked like if winter 2022/23 resembled each winter circulation storyline were created to explore counterfactuals of how bad the 2022 drought could have been over winter 2022/23 and beyond.
Dino Collalti, Nekeisha Spencer, and Eric Strobl
Nat. Hazards Earth Syst. Sci., 24, 873–890, https://doi.org/10.5194/nhess-24-873-2024, https://doi.org/10.5194/nhess-24-873-2024, 2024
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The risk of extreme rainfall events causing floods is likely increasing with climate change. Flash floods, which follow immediately after extreme rainfall, are particularly difficult to forecast and assess. We develop a decision rule for flash flood classification with data on all incidents between 2001 and 2018 in Jamaica with the statistical copula method. This decision rule tells us for any rainfall event of a certain duration how intense it has to be to likely trigger a flash flood.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
EGUsphere, https://doi.org/10.5194/egusphere-2023-3094, https://doi.org/10.5194/egusphere-2023-3094, 2024
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We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern U.S., in climate data. Analyzing the Susquehanna River Basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Ivan Vorobevskii, Thi Thanh Luong, and Rico Kronenberg
Nat. Hazards Earth Syst. Sci., 24, 681–697, https://doi.org/10.5194/nhess-24-681-2024, https://doi.org/10.5194/nhess-24-681-2024, 2024
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This study presents a new version of a framework which allows us to model water balance components at any site on a local scale. Compared with the first version, the second incorporates new datasets used to set up and force the model. In particular, we highlight the ability of the framework to provide seasonal forecasts. This gives potential stakeholders (farmers, foresters, policymakers, etc.) the possibility to forecast, for example, soil moisture drought and thus apply the necessary measures.
Diego Fernández-Nóvoa, Alexandre M. Ramos, José González-Cao, Orlando García-Feal, Cristina Catita, Moncho Gómez-Gesteira, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 24, 609–630, https://doi.org/10.5194/nhess-24-609-2024, https://doi.org/10.5194/nhess-24-609-2024, 2024
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The present study focuses on an in-depth analysis of floods in the lower section of the Tagus River from a hydrodynamic perspective by means of the Iber+ numerical model and on the development of dam operating strategies to mitigate flood episodes using the exceptional floods of February 1979 as a benchmark. The results corroborate the model's capability to evaluate floods in the study area and confirm the effectiveness of the proposed strategies to reduce flood impact in the lower Tagus valley.
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
EGUsphere, https://doi.org/10.5194/egusphere-2024-421, https://doi.org/10.5194/egusphere-2024-421, 2024
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Drought is a creeping phenomenon, but it is often still analysed and managed like an event without taking into consideration what happened before and after. In this paper we review the literature and discuss five cases, where drought, its impacts and responses develop differently over time. We look at the hydrological, ecological and social system and their connections. And we provide suggestions for further research and for monitoring, modelling and management.
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
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We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Andrea Abbate, Leonardo Mancusi, Francesco Apadula, Antonella Frigerio, Monica Papini, and Laura Longoni
Nat. Hazards Earth Syst. Sci., 24, 501–537, https://doi.org/10.5194/nhess-24-501-2024, https://doi.org/10.5194/nhess-24-501-2024, 2024
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CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) is a new physically based and spatially distributed rainfall-runoff model. The main novelties consist of reproducing rainfall-induced geo-hydrological hazards such as shallow landslide, debris flow and watershed erosion through a multi-hazard approach. CRHyME was written in Python, works at a high spatial and temporal resolution, and is a tool suitable for quantifying extreme rainfall consequences at the basin scale.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
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We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Miroslav Spano and Jaromir Riha
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-21, https://doi.org/10.5194/nhess-2024-21, 2024
Revised manuscript accepted for NHESS
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Our study examines how building the Skalička Dam near the Hranice Karst affects local groundwater. We used advanced modeling to analyze two dam layouts: lateral and through-flow reservoirs. Results show the through-flow variant significantly alters water levels and mineral water discharge, while the lateral layout has less impact.
Luis Cea, Manuel Álvarez, and Jerónimo Puertas
Nat. Hazards Earth Syst. Sci., 24, 225–243, https://doi.org/10.5194/nhess-24-225-2024, https://doi.org/10.5194/nhess-24-225-2024, 2024
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Mozambique is highly exposed to the impact of floods. To reduce flood damage, it is necessary to develop mitigation measures. Hydrological software is a very useful tool for that purpose, since it allows for a precise quantification of flood hazard in different scenarios. We present a methodology to quantify flood hazard in data-scarce regions, using freely available data and software, and we show its potential by analysing the flood event that took place in the Umbeluzi Basin in February 2023.
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024, https://doi.org/10.5194/nhess-24-199-2024, 2024
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This work describes Flood-PROOFS East Africa, an impact-based flood forecasting system for the Greater Horn of Africa. It is based on hydrological simulations, inundation mapping, and estimation of population and assets exposed to upcoming river floods. The system supports duty officers in African institutions in the daily monitoring of hydro-meteorological disasters. A first evaluation shows the system performance for the catastrophic floods in the Nile River basin in summer 2020.
María Carmen Llasat, Montserrat Llasat-Botija, Erika Pardo, Raül Marcos-Matamoros, and Marc Lemus-Canovas
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-206, https://doi.org/10.5194/nhess-2023-206, 2024
Revised manuscript accepted for NHESS
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Climate change is leading in the Pyrenees Massif to a change in socioeconomic increasing their sensitivity to natural risks such as floods. However, until now, no systematic study like this one had been carried out that would allow evaluating the frequency, distribution and main meteorological features of these events on a massif scale. In 35 years there have been 181 flood events that have produced 154 fatalities.
Nejc Bezak, Panos Panagos, Leonidas Liakos, and Matjaž Mikoš
Nat. Hazards Earth Syst. Sci., 23, 3885–3893, https://doi.org/10.5194/nhess-23-3885-2023, https://doi.org/10.5194/nhess-23-3885-2023, 2023
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Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.
Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, Gerald Corzo, and Dimitri Solomatine
Nat. Hazards Earth Syst. Sci., 23, 3863–3883, https://doi.org/10.5194/nhess-23-3863-2023, https://doi.org/10.5194/nhess-23-3863-2023, 2023
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This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.
Bouchra Zellou, Nabil El Moçayd, and El Houcine Bergou
Nat. Hazards Earth Syst. Sci., 23, 3543–3583, https://doi.org/10.5194/nhess-23-3543-2023, https://doi.org/10.5194/nhess-23-3543-2023, 2023
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In this study, we underscore the critical importance of strengthening drought prediction capabilities in the Mediterranean region. We present an in-depth evaluation of current drought forecasting approaches, encompassing statistical, dynamical, and hybrid statistical–dynamical models, and highlight unexplored research opportunities. Additionally, we suggest viable directions to enhance drought prediction and early warning systems within the area.
Francisco Rodrigues do Amaral, Nicolas Gratiot, Thierry Pellarin, and Tran Anh Tu
Nat. Hazards Earth Syst. Sci., 23, 3379–3405, https://doi.org/10.5194/nhess-23-3379-2023, https://doi.org/10.5194/nhess-23-3379-2023, 2023
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We propose an in-depth analysis of typhoon-induced compound flood drivers in the megacity of Ho Chi Minh, Vietnam. We use in situ and satellite measurements throughout the event to form a holistic overview of its impact. No evidence of storm surge was found, and peak precipitation presents a 16 h time lag to peak river discharge, which evacuates only 1.5 % of available water. The astronomical tide controls the river level even during the extreme event, and it is the main urban flood driver.
Juliette Godet, Olivier Payrastre, Pierre Javelle, and François Bouttier
Nat. Hazards Earth Syst. Sci., 23, 3355–3377, https://doi.org/10.5194/nhess-23-3355-2023, https://doi.org/10.5194/nhess-23-3355-2023, 2023
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This article results from a master's research project which was part of a natural hazards programme developed by the French Ministry of Ecological Transition. The objective of this work was to investigate a possible way to improve the operational flash flood warning service by adding rainfall forecasts upstream of the forecasting chain. The results showed that the tested forecast product, which is new and experimental, has a real added value compared to other classical forecast products.
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
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In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
Clément Houdard, Adrien Poupardin, Philippe Sergent, Abdelkrim Bennabi, and Jena Jeong
Nat. Hazards Earth Syst. Sci., 23, 3111–3124, https://doi.org/10.5194/nhess-23-3111-2023, https://doi.org/10.5194/nhess-23-3111-2023, 2023
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We developed a system able to to predict, knowing the appropriate characteristics of the flood defense structure and sea state, the return periods of potentially dangerous events as well as a ranking of parameters by order of uncertainty.
The model is a combination of statistical and empirical methods that have been applied to a Mediterranean earthen dike. This shows that the most important characteristics of the dyke are its geometrical features, such as its height and slope angles.
Maryam Pakdehi, Ebrahim Ahmadisharaf, Behzad Nazari, and Eunsaem Cho
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-152, https://doi.org/10.5194/nhess-2023-152, 2023
Revised manuscript accepted for NHESS
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Machine learning (ML) models have growingly received attention for predicting flood events. However, there has been concerns about the transferability of these models (their capability in predicting out-of-sample events). Here, we showed that ML models can be transferable for hindcasting maximum river flood depths across major events (Hurricanes Ida, Isaias, Sandy, and Irene) in coastal watersheds when informed by the spatial distribution of pertinent features and underlying physical processes.
Lisa Köhler, Torsten Masson, Sabrina Köhler, and Christian Kuhlicke
Nat. Hazards Earth Syst. Sci., 23, 2787–2806, https://doi.org/10.5194/nhess-23-2787-2023, https://doi.org/10.5194/nhess-23-2787-2023, 2023
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We analyzed the impact of flood experience on adaptive behavior and self-reported resilience. The outcomes draw a paradoxical picture: the most experienced people are the most adapted but the least resilient. We find evidence for non-linear relationships between the number of floods experienced and resilience. We contribute to existing knowledge by focusing specifically on the number of floods experienced and extending the rare scientific literature on the influence of experience on resilience.
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023, https://doi.org/10.5194/nhess-23-2769-2023, 2023
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Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
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We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
Maliko Tanguy, Michael Eastman, Eugene Magee, Lucy J. Barker, Thomas Chitson, Chaiwat Ekkawatpanit, Daniel Goodwin, Jamie Hannaford, Ian Holman, Liwa Pardthaisong, Simon Parry, Dolores Rey Vicario, and Supattra Visessri
Nat. Hazards Earth Syst. Sci., 23, 2419–2441, https://doi.org/10.5194/nhess-23-2419-2023, https://doi.org/10.5194/nhess-23-2419-2023, 2023
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Droughts in Thailand are becoming more severe due to climate change. Understanding the link between drought impacts on the ground and drought indicators used in drought monitoring systems can help increase a country's preparedness and resilience to drought. With a focus on agricultural droughts, we derive crop- and region-specific indicator-to-impact links that can form the basis of targeted mitigation actions and an improved drought monitoring and early warning system in Thailand.
Leon Scheiber, Mazen Hoballah Jalloul, Christian Jordan, Jan Visscher, Hong Quan Nguyen, and Torsten Schlurmann
Nat. Hazards Earth Syst. Sci., 23, 2313–2332, https://doi.org/10.5194/nhess-23-2313-2023, https://doi.org/10.5194/nhess-23-2313-2023, 2023
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Numerical models are increasingly important for assessing urban flooding, yet reliable input data are oftentimes hard to obtain. Taking Ho Chi Minh City as an example, this paper explores the usability and reliability of open-access data to produce preliminary risk maps that provide first insights into potential flooding hotspots. As a key novelty, a normalized flood severity index is presented which combines flood depth and duration to enhance the interpretation of hydro-numerical results.
Claudia Herbert and Petra Döll
Nat. Hazards Earth Syst. Sci., 23, 2111–2131, https://doi.org/10.5194/nhess-23-2111-2023, https://doi.org/10.5194/nhess-23-2111-2023, 2023
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This paper presents a new method for selecting streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems in large-scale drought early warning systems. Indicators are classified by their inherent assumptions about the habituation of people and ecosystems to the streamflow regime and their level of drought characterization, namely drought magnitude (water deficit at a certain point in time) and severity (cumulated magnitude since drought onset).
Maryse Charpentier-Noyer, Daniela Peredo, Axelle Fleury, Hugo Marchal, François Bouttier, Eric Gaume, Pierre Nicolle, Olivier Payrastre, and Maria-Helena Ramos
Nat. Hazards Earth Syst. Sci., 23, 2001–2029, https://doi.org/10.5194/nhess-23-2001-2023, https://doi.org/10.5194/nhess-23-2001-2023, 2023
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This paper proposes a methodological framework designed for event-based evaluation in the context of an intense flash-flood event. The evaluation adopts the point of view of end users, with a focus on the anticipation of exceedances of discharge thresholds. With a study of rainfall forecasts, a discharge evaluation and a detailed look at the forecast hydrographs, the evaluation framework should help in drawing robust conclusions about the usefulness of new rainfall ensemble forecasts.
Min Li, Mingfeng Zhang, Runxiang Cao, Yidi Sun, and Xiyuan Deng
Nat. Hazards Earth Syst. Sci., 23, 1453–1464, https://doi.org/10.5194/nhess-23-1453-2023, https://doi.org/10.5194/nhess-23-1453-2023, 2023
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It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
Cited articles
Abebe, A. and Price, R.: Decision support system for urban flood management,
J. Hydroinform., 7, 3–15, https://doi.org/10.2166/hydro.2005.0002, 2005.
Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., and
Lallahem, S.: River flow model using artificial neural networks, Energy Proced., 74, 1007–1014, https://doi.org/10.1016/j.egypro.2015.07.832, 2015.
Amirkhani, H. and Rahmati, M.: Expectation maximization based ordering
aggregation for improving the K2 structure learning algorithm, Intell. Data Anal., 19, 1003–1018, https://doi.org/10.3233/ida-150755, 2015.
ASCE: Task Committee on Application of Artificial Neural Networks in
Hydrology: Artificial neural networks in hydrology. II: hydrologic
applications, J. Hydrol. Eng., 5, 124–137, 2000.
Banihabib, M. and Arabi, A.: The impact of catchment management on emergency
management of flash-flood, International Journal of Emergency Management,
12, 185–195, https://doi.org/10.1504/ijem.2016.076618, 2016.
Banihabib, M. E., Arabi, A., and Salha, A. A.: A dynamic artificial neural
network for assessment of land-use change impact on warning lead-time of
flood, International Journal of Hydrology Science and Technology, 5,
163–178, https://doi.org/10.1504/ijhst.2015.070093, 2015.
Bergmann, K. and Kopp, S.: GNetIc–Using bayesian decision networks for
iconic gesture generation, International Workshop on Intelligent Virtual
Agents, Springer, Berlin, Heidelberg, 76–89, 2009.
Chen, F. and Dudhia, J.: Coupling an advanced land surface–hydrology model
with the Penn State–NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0587:caalsh>2.0.co;2,
2001.
Cheng, K.-S., Lien, Y.-T., Wu, Y.-C., and Su, Y.-F.: On the criteria of
model performance evaluation for real-time flood forecasting,
Stoch. Env. Res. Risk A., 31, 1123–1146, 2017.
Chitsaz, N. and Banihabib, M. E.: Comparison of different multi criteria
decision-making models in prioritizing flood management alternatives, Water Resour. Manag., 29, 2503–2525, 2015.
Cooper, G. F. and Herskovits, E.: A Bayesian method for the induction of
probabilistic networks from data, Mach. Learn., 9, 309–347, https://doi.org/10.1002/0470091754.ch2, 1992.
Deb, S., Srivastava, T., and Kishtawal, C.: The WRF model performance for
the simulation of heavy precipitating events over Ahmedabad during August
2006, J. Earth Syst. Sci., 117, 589–602, https://doi.org/10.1007/s12040-008-0055-5, 2008.
Dietrich, J., Schumann, A. H., Redetzky, M., Walther, J., Denhard, M., Wang, Y., Pfützner, B., and Büttner, U.: Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions, Nat. Hazards Earth Syst. Sci., 9, 1529–1540, https://doi.org/10.5194/nhess-9-1529-2009, 2009a.
Dietrich, J., Denhard, M., and Schumann, A.: Can ensemble forecasts improve
the reliability of flood alerts?, J. Flood Risk Manag., 2,
232–242, https://doi.org/10.1111/j.1753-318x.2009.01039.x, 2009b.
Doycheva, K., Horn, G., Koch, C., Schumann, A., and König, M.:
Assessment and weighting of meteorological ensemble forecast members based
on supervised machine learning with application to runoff simulations and
flood warning, Adv. Eng. Inform., 33, 427–439, https://doi.org/10.1016/j.aei.2016.11.001, 2017.
Dudhia, J.: Numerical study of convection observed during the winter monsoon
experiment using a mesoscale two-dimensional model, J. Atmos. Sci.,
46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:nsocod>2.0.co;2, 1989.
El Afandi, G., Morsy, M., and El Hussieny, F.: Heavy rainfall simulation
over sinai peninsula using the weather research and forecasting model,
International Journal of Atmospheric Sciences, 2013, 241050, https://doi.org/10.1155/2013/241050, 2013.
Goodarzi, L., Banihabib, M. E., and Roozbahani, A.: A decision-making model
for flood warning system based on ensemble forecasts, J.
Hydrol., 573, 207–219, https://doi.org/10.1016/j.jhydrol.2019.03.040, 2019.
Grell, G. A.: Prognostic evaluation of assumptions used by cumulus
parameterizations, Mon. Weather Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:peoaub>2.0.co;2, 1993.
Grell, G. A. and Dévényi, D.: A generalized approach to parameterizing
convection combining ensemble and data assimilation techniques, Geophys.
Res. Lett., 29, 38-1–8-4, https://doi.org/10.1029/2002gl015311, 2002.
Hénonin, J., Russo, B., Roqueta, D. S., Sanchez-Diezma, R., Domingo, N.,
Thomsen, F., and Mark, O.: Urban flood real-time forecasting and modelling:
a state-of-the-art review, Proceedings, MIKE by DHI conference, https://doi.org/10.2166/hydro.2013.132, 2010.
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice
microphysical processes for the bulk parameterization of clouds and
precipitation, Mon. Weather Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:aratim>2.0.co;2,
2004.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/mwr3199.1, 2006.
Janjić, Z. I.: The step-mountain eta coordinate model: Further
developments of the convection, viscous sublayer, and turbulence closure
schemes, Mon. Weather Rev., 122, 927–945,
https://doi.org/10.1175/1520-0493(1994)122<0927:tsmecm>2.0.co;2, 1994.
Jensen, F. V.: Bayesian Networks and Decision Graphs, Springer-Verlag, New
York, ISBN 0-387-95259-4, 2001.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining plume
model and its application in convective parameterization, J. Atmos. Sci.,
47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:aodepm>2.0.co;2, 1990.
Kerkhoven, E., Gan, T. Y., Shiiba, M., Reuter, G., and Tanaka, K.: A
comparison of cumulus parameterization schemes in a numerical weather
prediction model for a monsoon rainfall event, Hydrol. Process., 20,
1961–1978, https://doi.org/10.1002/hyp.5967, 2006.
Khan, M. S. and Coulibaly, P.: Bayesian neural network for rainfall-runoff
modelling, Water Resour. Res., 42, W07409, https://doi.org/10.1029/2005WR003971 2006.
Kontkanen, P., Myllymaki, P., Silander, T., and Tirri, H.: Comparing
predictive inference methods for discrete domains, Proceedings of the
sixth International Workshop on Artificial Intelligence and Statistics, Ft.
Lauderdale, USA, 311–318, 1997.
Lauritzen, S. L. and Spiegelhalter, D. J.: Local computations with
probabilities on graphical structures and the application to expert systems,
J. Roy. Stat. Soc. B, 50, 157–224, 1988.
Leandro, J., Gander, A., Beg, M. N. A., Bhola, P., Konnerth, I., Willems, W.,
Carvalho, R., and Disse, M.: Forecasting upper and lower uncertainty bands
of river flood discharges with high predictive skill, J. Hydrol.,
576, 749–763, https://doi.org/10.1016/j.jhydrol.2019.06.052, 2019.
Li, J., Chen, Y., Wang, H., Qin, J., Li, J., and Chiao, S.: Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model, Hydrol. Earth Syst. Sci., 21, 1279–1294, https://doi.org/10.5194/hess-21-1279-2017, 2017.
Li, L., Li, W., and Jin, J.: Improvements in WRF simulation skills of south
eastern United States summer rainfall: physical parameterization and
horizontal resolution, Clim. Dynam., 43, 2077–2091, https://doi.org/10.1007/s00382-013-2031-2, 2014.
Li, W., Poupart, P., and van Beek, P.: Exploiting structure in weighted
model counting approaches to probabilistic inference, J. Artif. Intell. Res., 40, 729–765, 2011.
Liang, X. Z., Li, L., Dai, A., and Kunkel, K. E.: Regional climate model
simulation of summer precipitation diurnal cycle over the United States,
Geophys. Res. Lett., 29, L24208, https://doi.org/10.1029/2004gl021054, 2004.
Madigan, D., York, J., and Allard, D.: Bayesian graphical models for
discrete data, International Statistical Review/Revue Internationale de
Statistique, 63, 215–232, https://doi.org/10.2307/1403615, 1995.
Mammadov, M., Yazici, B., Yolaçan, Ş., Aslanargun, A.,
Yüzer, A., and Ağaoğlu, E.: Statistical Methods
and Artificial Neural Networks, Journal of Modern Applied Statistical Methods, 5, 495–512, 2005.
Mediero, L., Garrote, L., and Martin-Carrasco, F.: A probabilistic model to
support reservoir operation decisions during flash floods,
Hydrolog. Sci. J., 52, 523–537, https://doi.org/10.1623/hysj.52.3.523, 2007.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1023/b:asys.0000017241.50767.32,
1997.
Myllymaki, P., Silander, T., Tirri, H., and Uronen, P.: B-Course: a
web-based tool for Bayesian and causal data analysis, Int. J. Artif. Intell.
T., 11, 369–387, https://doi.org/10.1142/s0218213002000940, 2002.
National Centers for Environmental Prediction/National Weather Service/NOAA/US Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colorado, https://doi.org/10.5065/D6M043C6, 2000.
Pennelly, C., Reuter, G., and Flesch, T.: Verification of the WRF model for
simulating heavy precipitation in Alberta, Atmos. Res., 135,
172–192, https://doi.org/10.1016/j.atmosres.2013.09.004, 2014.
Phan, T. D, Smart, J. C. R., Capon, S. J., Hadwen, W. L., and Sahin, O.:
Applications of Bayesian belief networks in water resource management: A
systematic, Environ. Modell. Softw., 85, 98–111, 2016.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using
Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.21236/ada459828, 2005.
Raju, M. M., Srivastava, R. K., Bisht, D., Sharma, H. C., and Kumar, A.:
Development of artificial neural-network-based models for the simulation of
spring discharge, Adv. Art. Int., 1–11, 2011.
Reed, E. and Mengshoel, O. J.: Bayesian network parameter learning using EM
with parameter sharing, Proceedings of the Eleventh UAI Conference on
Bayesian Modeling Applications Workshop-Volume, 27 July 2014, Quebec, Canada,
1218, 48–59, 2014.
Rogelis, M. C. and Werner, M.: Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas, Hydrol. Earth Syst. Sci., 22, 853–870, https://doi.org/10.5194/hess-22-853-2018, 2018.
Sarkar, A. and Kumar, R.: Artificial neural networks for event based
rainfall-runoff modelling, Journal of Water Resource and Protection, 4,
891–897, https://doi.org/10.4236/jwarp.2012.410105, 2012.
Scutari, M.: Bayesian network constraint-based structure learning
algorithms: Parallel and optimised implementations in the bnlearn R package,
J. Stat. Softw., 7, 1–20, 2017.
Sharma, V., Rai, S., and Dev, A.: A comprehensive study of artificial neural
networks, International Journal of Advanced Research in Computer Science and
Software Engineering, 2, 278–284, 2012.
Sharma, A. and Goyal, M. K.: Bayesian network for monthly rainfall forecast:
a comparison of K2 and MCMC algorithm, International Journal of Computers and Applications, 38, 199–206, https://doi.org/10.1080/1206212x.2016.1237131, 2016.
Shin, J. Y., Ajmal, M., Yoo, J., and Kim, T.-W.: A Bayesian network-based
probabilistic framework for drought forecasting and outlook, Adv. Meteorol.,
2016, 9472605, https://doi.org/10.1155/2016/9472605, 2016.
Sikder, S. and Hossain, F.: Assessment of the weather research and
forecasting model generalized parameterization schemes for advancement of
precipitation forecasting in monsoon-driven river basins, J. Adv. Model. Earth Sy., 8, 1210–1228, https://doi.org/10.1002/2016ms000678,
2016.
Tennant, W. J., Toth, Z., and Rae, K. J.: Application of the NCEP ensemble
prediction system to medium-range forecasting in South Africa: New products,
benefits, and challenges, Weather Forecast., 22, 18–35, 2007.
Uusitalo, L.: Advantages and challenges of Bayesian networks in
environmental modelling, Ecol. Model., 203, 312–318, 2007.
Yang, B., Qian, Y., Lin, G., Leung, R., and Zhang, Y.: Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model, Atmos. Chem. Phys., 12, 2409–2427, https://doi.org/10.5194/acp-12-2409-2012, 2012.
Yang, T.-H., Hwang, G.-D., Tsai, C.-C., and Ho, J.-Y.: Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts, Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, 2016.
Zhang, R. and Bivens, A. J.: Comparing the use of Bayesian networks and
neural networks in response time modeling for service-oriented systems,
Proceedings of the 2007 workshop on Service-oriented computing performance:
aspects, issues and approaches, 67–74, https://doi.org/10.1145/1272457.1272467, 2007.
Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., and Niyogl, D.:
Improving high-resolution weather forecasts using the Weather Research and
Forecasting (WRF) Model with an updated Kain–Fritsch scheme, Mon. Weather Rev., 144, 833–860, https://doi.org/10.1175/mwr-d-15-0005.1, 2016.
Short summary
We developed a novel approach in using Bayesian networks (BNs) for ensemble flood forecasting in a case study in Iran. This allows fast early warning without the need for hydrological modelling. We recommend to combine precipitation ensembles with hydrological initial conditions in the BN. The number of observed flood events is low by nature. Under the limited amount of data, BN outperformed artificial neural networks with good results. Future work will validate the concept further.
We developed a novel approach in using Bayesian networks (BNs) for ensemble flood forecasting in...
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